Cluster (level 2): NBC_MBC
2021/03/15
1 Introduction
The objective of this notebook is to cluster cells at a low resolution that allows us to “fetch” the clusters that are potential doublets, so that we can easily exclude them.
2 Pre-processing
2.1 Load packages
library(Seurat)
library(tidyverse)2.2 Parameters
# Paths
path_to_obj <- str_c(
here::here("scRNA-seq/results/R_objects/level_2/"),
cell_type,
"/",
cell_type,
"_integrated_level_2.rds",
sep = ""
)
# Functions
source(here::here("scRNA-seq/bin/utils.R"))
# Colors
color_palette <- c("black", "gray", "red", "yellow", "violet", "green4",
"blue", "chocolate1", "coral2", "blueviolet",
"brown1", "darkmagenta", "deepskyblue1", "dimgray",
"deeppink1", "green", "lightgray", "hotpink1",
"indianred4", "khaki", "mediumorchid2")2.3 Load data
# Seurat object
seurat <- readRDS(path_to_obj)
seurat## An object of class Seurat
## 37378 features across 126532 samples within 1 assay
## Active assay: RNA (37378 features, 0 variable features)
## 3 dimensional reductions calculated: pca, umap, harmony3 Cluster
resolutions <- c(0.025, 0.05, 0.1, 0.2, 0.3, 0.4)
seurat <- FindClusters(seurat, resolution = resolutions)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 126532
## Number of edges: 2743967
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9750
## Number of communities: 29
## Elapsed time: 48 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 126532
## Number of edges: 2743967
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9560
## Number of communities: 30
## Elapsed time: 48 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 126532
## Number of edges: 2743967
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9362
## Number of communities: 32
## Elapsed time: 48 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 126532
## Number of edges: 2743967
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9154
## Number of communities: 36
## Elapsed time: 52 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 126532
## Number of edges: 2743967
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8972
## Number of communities: 37
## Elapsed time: 53 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 126532
## Number of edges: 2743967
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8821
## Number of communities: 40
## Elapsed time: 49 secondsvars <- str_c("RNA_snn_res.", resolutions)
umap_clusters <- purrr::map(vars, function(x) {
p <- DimPlot(seurat, group.by = x, cols = color_palette)
p
})
umap_clusters## [[1]]
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4 Session Information
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.7 (Santiago)
##
## Matrix products: default
## BLAS: /apps/R/3.6.0/lib64/R/lib/libRblas.so
## LAPACK: /home/devel/rmassoni/anaconda3/lib/libmkl_rt.so
##
## locale:
## [1] LC_CTYPE=C LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.4 purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 Seurat_3.2.0 BiocStyle_2.14.4
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-25 ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2 fs_1.4.1 rstudioapi_0.11 spatstat.data_1.4-3 farver_2.0.3 leiden_0.3.3 listenv_0.8.0 ggrepel_0.8.2 fansi_0.4.1 lubridate_1.7.8 xml2_1.3.2 codetools_0.2-16 splines_3.6.0 knitr_1.28 polyclip_1.10-0 jsonlite_1.7.2 broom_0.5.6 ica_1.0-2 cluster_2.1.0 dbplyr_1.4.4 png_0.1-7 uwot_0.1.8 shiny_1.4.0.2 sctransform_0.2.1 BiocManager_1.30.10 compiler_3.6.0 httr_1.4.2 backports_1.1.7 assertthat_0.2.1 Matrix_1.2-18 fastmap_1.0.1 lazyeval_0.2.2 cli_2.0.2 later_1.0.0 htmltools_0.5.1.1 tools_3.6.0 rsvd_1.0.3 igraph_1.2.5 gtable_0.3.0 glue_1.4.1 RANN_2.6.1 reshape2_1.4.4 rappdirs_0.3.1 Rcpp_1.0.6 spatstat_1.64-1 cellranger_1.1.0 vctrs_0.3.6 ape_5.3 nlme_3.1-148
## [55] lmtest_0.9-37 xfun_0.14 globals_0.12.5 rvest_0.3.5 mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3 goftest_1.2-2 future_1.17.0 MASS_7.3-51.6 zoo_1.8-8 scales_1.1.1 hms_0.5.3 promises_1.1.0 spatstat.utils_1.17-0 parallel_3.6.0 RColorBrewer_1.1-2 yaml_2.2.1 reticulate_1.16 pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15 stringi_1.4.6 rlang_0.4.10 pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41 ROCR_1.0-11 tensor_1.5 labeling_0.3 patchwork_1.0.0 htmlwidgets_1.5.1 cowplot_1.0.0 tidyselect_1.1.0 here_0.1 RcppAnnoy_0.0.16 plyr_1.8.6 magrittr_1.5 bookdown_0.19 R6_2.4.1 generics_0.0.2 DBI_1.1.0 withr_2.4.1 pillar_1.4.4 haven_2.3.1 mgcv_1.8-31 fitdistrplus_1.1-1 survival_3.1-12 abind_1.4-5 future.apply_1.5.0 modelr_0.1.8 crayon_1.3.4 KernSmooth_2.23-17
## [109] plotly_4.9.2.1 rmarkdown_2.2 readxl_1.3.1 grid_3.6.0 data.table_1.12.8 blob_1.2.1 reprex_0.3.0 digest_0.6.20 xtable_1.8-4 httpuv_1.5.3.1 munsell_0.5.0 viridisLite_0.3.0